SOURCE-LINKED INTELLIGENCE
Verify Smarter, Evolve Further: Efficient Harness Evolution through Behavior-Aware Verification
Agent harnesses shape how language-model agents use instructions, tools, and runtime components, but adapting these harnesses requires costly verification. Existing propose-and-verify methods typically score every candidate on a fixed task set, wasting rollouts on unrelated behaviors and allowing aggregate scores to obscure specific regressions. We introduce HarnessLens, a budget-aware framework for automated harness evolution. HarnessLens jointly explores the task space and user-configurable components, derives candidate modifications from execution trajectories, and selectively verifies each
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-27T16:12:23.000Z
First collected: 2026-09-21T08:32:02.028Z. This is not the publication date.